100 citations as recorded by crossref.

1. Watershed zonation through hillslope clustering for tractably quantifying above- and below-ground watershed heterogeneity and functions H. Wainwright et al. 10.5194/hess-26-429-2022
2. Clustering of causal graphs to explore drivers of river discharge W. Günther et al. 10.1017/eds.2023.17
3. TOSSH: A Toolbox for Streamflow Signatures in Hydrology S. Gnann et al. 10.1016/j.envsoft.2021.104983
4. Regionalization with hierarchical hydrologic similarity and ex situ data in the context of groundwater recharge estimation at ungauged watersheds C. Chang & Y. Rubin 10.5194/hess-23-2417-2019
5. Tradeoff between economic and environmental costs and benefits of hydropower production at run-of-river-diversion schemes under different environmental flows scenarios M. Bejarano et al. 10.1016/j.jhydrol.2019.03.048
6. Improvement of Regional Clustering Using Flood Control Characteristics and t-SNE of Machine Learning H. Joo et al. 10.9798/KOSHAM.2020.20.3.247
7. Evaluation of Catchments’ Similarity by Penalization in the Context of Engineering Tasks—A Case Study of Four Slovakian Catchments M. Cisty et al. 10.3390/w13202894
8. Hydrological impacts of a wildfire in a Boreal region: The Västmanland fire 2014 (Sweden) R. Pimentel & B. Arheimer 10.1016/j.scitotenv.2020.143519
9. Landscape and climate conditions influence the hydrological sensitivity to climate change in eastern Canada O. Aygün et al. 10.1016/j.jhydrol.2022.128595
10. Factors controlling alterations in the performance of a runoff model in changing climate conditions P. Sleziak et al. 10.2478/johh-2018-0031
11. River widening in mountain and foothill areas during floods: Insights from a meta-analysis of 51 European Rivers V. Ruiz-Villanueva et al. 10.1016/j.scitotenv.2023.166103
12. LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften C. Klingler et al. 10.1007/s00506-021-00769-x
13. On the selection of precipitation products for the regionalisation of hydrological model parameters O. Baez-Villanueva et al. 10.5194/hess-25-5805-2021
14. Classification and Zoning of Rivers by Their Water Regime: History, Methodology, and Perspectives N. Frolova et al. 10.1134/S0097807821020056
15. Evaluation of parameter sensitivity of a rainfall-runoff model over a global catchment set L. Santos et al. 10.1080/02626667.2022.2035388
16. Linking hydrologic signatures to hydrologic processes: A review H. McMillan 10.1002/hyp.13632
17. Integrating scientific knowledge into machine learning using interactive decision trees G. Sarailidis et al. 10.1016/j.cageo.2022.105248
18. Towards parameter estimation in global hydrological models J. Kupzig et al. 10.1088/1748-9326/acdae8
19. Identification of Seasonal Streamflow Regimes and Streamflow Drivers for Daily and Peak Flows in Alaska J. Curran & F. Biles 10.1029/2020WR028425
20. Machine learning based identification of dominant controls on runoff dynamics H. Oppel & A. Schumann 10.1002/hyp.13740
21. Hydrological characteristics of Australia: relationship between surface flow, climate and intrinsic catchment properties J. Jaffrés et al. 10.1016/j.jhydrol.2021.126911
22. Priority Research Topics to Improve Streamflow Data Availability in Data-Scarce Countries: The Case for Ethiopia M. Taye et al. 10.3390/hydrology10120220
23. Regulation of snow-fed rivers affects flow regimes more than climate change B. Arheimer et al. 10.1038/s41467-017-00092-8
24. Constraining Conceptual Hydrological Models With Multiple Information Sources R. Nijzink et al. 10.1029/2017WR021895
25. Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil L. Kuana et al. 10.5194/hess-28-3367-2024
26. Simple Catchments and Where to Find Them: The Storage-Discharge Relationship as a Proxy for Catchment Complexity F. Jehn et al. 10.3389/frwa.2021.631651
27. Investigating the spatial–temporal changes of flood events across the Yangtze River Basin, China: Identification, spatial heterogeneity, and dominant impact factors J. Yu et al. 10.1016/j.jhydrol.2023.129503
28. Geological Effects on the Scaling Relationships of Groundwater Contributions in Forested Watersheds K. Iwasaki et al. 10.1029/2021WR029641
29. A Ranking of Hydrological Signatures Based on Their Predictability in Space N. Addor et al. 10.1029/2018WR022606
30. Location, location, location – Considering relative catchment location to understand subsurface losses M. Kiraz-Safari et al. 10.1016/j.jhydrol.2024.132328
31. What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts? I. Pechlivanidis et al. 10.1029/2019WR026987
32. Hydrological similarity of catchments in the southern area of Colombian Andean region G. Carrillo-Soto et al. 10.1088/1742-6596/1388/1/012042
33. When good signatures go bad: Applying hydrologic signatures in large sample studies H. McMillan et al. 10.1002/hyp.14987
34. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al. 10.5194/essd-13-4529-2021
35. A Brief Analysis of Conceptual Model Structure Uncertainty Using 36 Models and 559 Catchments W. Knoben et al. 10.1029/2019WR025975
36. Strength and Memory of Precipitation's Control Over Streamflow Across the Conterminous United States E. Moges et al. 10.1029/2021WR030186
37. Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges N. Addor et al. 10.1080/02626667.2019.1683182
38. Stemflow infiltration hotspots near-tree stems along a soil depth gradient in a mixed oak–beech forest O. Hemr et al. 10.1007/s10342-023-01592-7
39. Spatial variability in specific discharge and streamwater chemistry during low flows: Results from snapshot sampling campaigns in eleven Swiss catchments M. Floriancic et al. 10.1002/hyp.13532
40. Intra-catchment comparison and classification of long-term streamflow variability in the Alps using wavelet analysis T. Pérez Ciria & G. Chiogna 10.1016/j.jhydrol.2020.124927
41. Understanding Catchments’ Hydrologic Response Similarity of Upper Blue Nile (Abay) basin through catchment classification Z. Tegegn et al. 10.1007/s40808-021-01298-y
42. Identifying Hydrologic Regimes and Drivers in Nova Scotia, Canada: Catchment Classification Efforts for a Data-Limited Region L. Johnston et al. 10.1061/(ASCE)HE.1943-5584.0002200
43. Similarity of catchment dynamics based on the interaction between streamflow and forcing time series: Use of a transfer entropy signature M. Neri et al. 10.1016/j.jhydrol.2022.128555
44. Understanding dominant controls on streamflow spatial variability to set up a semi-distributed hydrological model: the case study of the Thur catchment M. Dal Molin et al. 10.5194/hess-24-1319-2020
45. Unravelling runoff processes in Andean basins in northern Ecuador through hydrological signatures J. Gordillo & L. Pineda 10.1002/hyp.14354
46. Hunting for Information in Streamflow Signatures to Improve Modelled Drainage R. Schneider et al. 10.3390/w14010110
47. Hydrological characteristics of Australia: national catchment classification and regional relationships J. Jaffrés et al. 10.1016/j.jhydrol.2022.127969
48. Including Regional Knowledge Improves Baseflow Signature Predictions in Large Sample Hydrology S. Gnann et al. 10.1029/2020WR028354
49. Large-Scale Hydrological and Sediment Modeling in Nested Domains under Current and Changing Climate A. Bartosova et al. 10.1061/(ASCE)HE.1943-5584.0002078
50. How is Baseflow Index (BFI) impacted by water resource management practices? J. Bloomfield et al. 10.5194/hess-25-5355-2021
51. Lessons learnt from checking the quality of openly accessible river flow data worldwide L. Crochemore et al. 10.1080/02626667.2019.1659509
52. A global algorithm for identifying changing streamflow regimes: application to Canadian natural streams (1966–2010) M. Zaerpour et al. 10.5194/hess-25-5193-2021
53. Use of streamflow indices to identify the catchment drivers of hydrographs J. Mathai & P. Mujumdar 10.5194/hess-26-2019-2022
54. Incorporación de la conexión río acuífero y del flujo subterráneo con r.gwflow en el modelo hidrológico de Témez N. García-Bravo et al. 10.1080/23863781.2022.2037474
55. Catchment characteristics dominate the hydrological behavior of closed lakes across the Tibetan Plateau M. Hou et al. 10.1016/j.catena.2024.108090
56. Future streamflow regime changes in the United States: assessment using functional classification M. Brunner et al. 10.5194/hess-24-3951-2020
57. Probabilistic Description of Streamflow and Active Length Regimes in Rivers N. Durighetto et al. 10.1029/2021WR031344
58. Detecting Changes in River Flow Caused by Wildfires, Storms, Urbanization, Regulation, and Climate Across Sweden B. Arheimer & G. Lindström 10.1029/2019WR024759
59. Control of climate and physiography on runoff response behavior through use of catchment classification and machine learning S. Du et al. 10.1016/j.scitotenv.2023.166422
60. Regional Patterns and Physical Controls of Streamflow Generation Across the Conterminous United States S. Wu et al. 10.1029/2020WR028086
61. Global catchment modelling using World-Wide HYPE (WWH), open data, and stepwise parameter estimation B. Arheimer et al. 10.5194/hess-24-535-2020
62. Caravan - A global community dataset for large-sample hydrology F. Kratzert et al. 10.1038/s41597-023-01975-w
63. Stream gauge network grouping analysis using community detection H. Joo et al. 10.1007/s00477-020-01916-8
64. Which Potential Evapotranspiration Formula to Use in Hydrological Modeling World‐Wide? R. Pimentel et al. 10.1029/2022WR033447
65. Adaptively monitoring streamflow using a stereo computer vision system N. Hutley et al. 10.5194/hess-27-2051-2023
66. Identifying control factors of hydrological behavior through catchment classification in Mainland of China H. Xu et al. 10.1016/j.jhydrol.2024.132206
67. The response of forested upland micro-watersheds to extreme precipitation in a precipitation abundant year O. Hemr et al. 10.1007/s00704-023-04766-w
68. Using hydrological and climatic catchment clusters to explore drivers of catchment behavior F. Jehn et al. 10.5194/hess-24-1081-2020
69. QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al. 10.5194/essd-14-3715-2022
70. Toward reproducible and interoperable environmental modeling: Integration of HydroShare with server-side methods for exposing large-extent spatial datasets to models Y. Choi et al. 10.1016/j.envsoft.2024.106239
71. Repeating patterns in runoff time series: A basis for exploring hydrologic similarity of precipitation and catchment wetness conditions A. Hövel et al. 10.1016/j.jhydrol.2023.130585
72. On doing hydrology with dragons: Realizing the value of perceptual models and knowledge accumulation T. Wagener et al. 10.1002/wat2.1550
73. Clustering model responses in the frequency space for improved simulation‐based flood risk studies: The role of a cluster number A. Sikorska‐Senoner 10.1111/jfr3.12772
74. Evaluating low flow patterns, drivers and trends in the Delaware River Basin J. Hammond & B. Fleming 10.1016/j.jhydrol.2021.126246
75. Nonlinear control of climate, hydrology, and topography on streamflow response through the use of interpretable machine learning across the contiguous United States Y. Wu & N. Li 10.2166/wcc.2023.279
76. Run-Of-River Small Hydropower Plants as Hydro-Resilience Assets against Climate Change C. Skoulikaris 10.3390/su132414001
77. Technical note: Complexity–uncertainty curve (c-u-curve) – a method to analyse, classify and compare dynamical systems U. Ehret & P. Dey 10.5194/hess-27-2591-2023
78. Modeling streamflow variability at the regional scale: (1) perceptual model development through signature analysis F. Fenicia & J. McDonnell 10.1016/j.jhydrol.2021.127287
79. Streamflow Generation From Catchments of Contrasting Lithologies: The Role of Soil Properties, Topography, and Catchment Size D. Xiao et al. 10.1029/2018WR023736
80. What control the spatial patterns and predictions of runoff response over the contiguous USA? S. Jiang et al. 10.1007/s11442-024-2249-4
81. Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden M. Girons Lopez et al. 10.5194/hess-25-1189-2021
82. The persistence of snow on the ground affects the shape of streamflow hydrographs over space and time: a continental-scale analysis E. Le et al. 10.3389/fenvs.2023.1207508
83. A fuzzy hybrid clustering method for identifying hydrologic homogeneous regions S. Nadoushani et al. 10.2166/hydro.2018.004
84. On the relation between antecedent basin conditions and runoff coefficient for European floods C. Massari et al. 10.1016/j.jhydrol.2023.130012
85. Physiographic and Climatic Controls on Regional Groundwater Dynamics E. Haaf et al. 10.1029/2019WR026545
86. Classification of catchments for nitrogen using Artificial Neural Network Pattern Recognition and spatial data C. O'Sullivan et al. 10.1016/j.scitotenv.2021.151139
87. Mapping the spatial variability of rainfall from a physiographic-based multilinear regression: model development and application to the Southwestern Iberian Peninsula V. Ruiz-Ortiz et al. 10.1007/s10661-022-10312-4
88. Quantifying Snow Cover Distribution in Semiarid Regions Combining Satellite and Terrestrial Imagery R. Pimentel et al. 10.3390/rs9100995
89. CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain G. Coxon et al. 10.5194/essd-12-2459-2020
90. Long‐Term Nitrate Trajectories Vary by Season in Western European Catchments P. Ebeling et al. 10.1029/2021GB007050
91. Identification of homogeneous regions in terms of flood seasonality using a complex network approach W. Yang et al. 10.1016/j.jhydrol.2019.06.082
92. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia K. Fowler et al. 10.5194/essd-13-3847-2021
93. Detecting changes in water level caused by climate, land cover and dam construction in interconnected river−lake systems J. Liang et al. 10.1016/j.scitotenv.2021.147692
94. Characterizing Watersheds to Support Land-use Planning in Indonesia: A Case Study of Brantas Tropical Watershed B. Wiwoho et al. 10.1016/j.ecohyd.2023.06.001
95. Index‐Based Characterization and Quantification of Groundwater Dynamics B. Heudorfer et al. 10.1029/2018WR024418
96. Megafloods in Europe can be anticipated from observations in hydrologically similar catchments M. Bertola et al. 10.1038/s41561-023-01300-5
97. A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers T. Iliopoulou et al. 10.5194/hess-23-73-2019
98. Achieving Robust and Transferable Performance for Conservation‐Based Models of Dynamical Physical Systems F. Zheng et al. 10.1029/2021WR031818
99. A Quantitative Hydrological Climate Classification Evaluated With Independent Streamflow Data W. Knoben et al. 10.1029/2018WR022913
100. Floods and rivers: a circular causality perspective G. Sofia & E. Nikolopoulos 10.1038/s41598-020-61533-x